EURASIP Journal on Applied Signal Processing
Recurrent neural network based BER prediction for NLOS channels
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
An On-Line Learning Radial Basis Function Network and Its Application
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
On-Line Tuning of a Neural PID Controller Based on Variable Structure RBF Network
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Recurrent neural network based bit error rate prediction for narrowband fading channel
CSN '07 Proceedings of the Sixth IASTED International Conference on Communication Systems and Networks
An intelligent PID controller based on variable structure radial basis function network
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation
Computers in Biology and Medicine
Information Processing and Management: an International Journal
Hi-index | 0.00 |
We present a method of modifying the structure of radial basis function (RBF) network to work with nonstationary series that exhibit homogeneous nonstationary behavior. In the original RBF network, the hidden node's function is to sense the trajectory of the time series and to respond when there is a strong correlation between the input pattern and the hidden node's center. This type of response, however, is highly sensitive to changes in the level and trend of the time series. To counter these effects, the hidden node's function is modified to one which detects and reacts to the gradient of the series. We call this new network the gradient RBF (GRBF) model. Single and multistep predictive performance for the Mackey-Glass chaotic time series were evaluated using the classical RBF and GRBF models. The simulation results for the series without and with a tine-varying mean confirm the superior performance of the GRBF predictor over the RBF predictor